1. Field of the Invention
The invention generally relates to calculating speed and direction of travel in a positioning system, and, more specifically, the use of WLAN access points signals to calculate speed and direction of travel.
2. Description of Related Art
Position, speed of travel, and direction of travel (i.e. bearing) are basic components of navigation systems and any Location Based Services (LBS). Speed and bearing estimation are not only reported to the end users, but they are also used by the location services and systems to rectify position estimation. The information may also be used by navigation applications to translate a distance to a time duration. Location and speed estimation are also used extensively in cellular networks to optimize system parameters, like dynamic channel assignment and handover algorithms.
Outdoor and indoor WLAN based positioning systems have been explored by a couple of research labs, but none of them included speed and bearing estimation in their system. The most important research efforts in this area have been conducted by PlaceLab (www.placelab.com, a project sponsored by Microsoft and Intel), University of California San Diego ActiveCampus project (ActiveCampus—Sustaining Educational Communities through Mobile Technology, technical report #CS2002-0714), and the MIT campus wide location system, and it was evaluated through several small projects at Dartmouth college (e.g., M. Kim, J. J. Fielding, and D. Kotz, “Risks of using AP locations discovered through war driving”).
There have been a number of commercial offerings of Wi-Fi location systems targeted at indoor positioning. (See, e.g., Kavitha Muthukrishnan, Maria Lijding, Paul Having a, Towards Smart Surroundings: Enabling Techniques and Technologies for Localization, Proceedings of the International Workshop on Location and Context-Awareness (LoCA 2005) at Pervasive 2005, May 2005, and Hazas, M., Scott, J., Krumm, J.: Location-Aware Computing Comes of Age, IEEE Computer, 37(2):95-97, Feb. 2004 005, Pa005, Pages 350-362.) These systems are designed to address asset and people tracking within a controlled environment like a corporate campus, a hospital facility or a shipping yard. The classic example is having a system that can monitor the exact location of the crash cart within the hospital so that when there is a cardiac arrest the hospital staff doesn't waste time locating the device. The accuracy requirements for these use cases are very demanding, typically calling for 1-3 meter accuracy. These systems use a variety of techniques to fine tune their accuracy including conducting detailed site surveys of every square foot of the campus to measure radio signal propagation. They also require a constant network connection so that the access point and the client radio can exchange synchronization information similar to how A-GPS works. While these systems are becoming more reliable for indoor use cases, they are ineffective in any wide-area deployment. It is impossible to conduct the kind of detailed site survey required across an entire city and there is no way to rely on a constant communication channel with 802.11 access points across an entire metropolitan area to the extent required by these systems. Most importantly, outdoor radio propagation is fundamentally different than indoor radio propagation, rendering these indoor positioning algorithms almost useless in a wide-area scenario.
Speed estimation of WLAN users has drawn little attention. There is one paper related to this topic, which covers the indoor environment. Using power variation of signal strength of WLANs to detect stationary users inside a building was proposed by Krumm and Harvitz. (See John Krumm and Eric Harvitz, “LOCADIO: Inferring Motion and Location from WLAN Signal Strengths” Proc. Of Mobiquitous, pp 4-14, Aug. 22-26, 2004). The proposed algorithm is based on measuring the variance of the signal strength of the strongest access point in an indoor environment. In this article, the user's movement is captured in two states, which are “still” and “moving” states. The proposed algorithm to detect still state is based on detailed survey of the building and finding transition probability between “still” and “moving” states as a function of the building's floor plan. This article uses standard deviation of power samples to capture the power variation. In addition, a detailed site survey was required.
Estimation of speed of travel by using radio wave propagation characteristics is not a new idea, and different methods have been suggested to calculate user speed by using radio waves. The common way of estimating speed of a receiver relative to the transmitter is based on measuring the Doppler frequency. Estimating speed based on Doppler frequency needs the exact knowledge of the transmit frequency and angle of arrival of radio waves.
Due to shadowing and multi-path effects, conventional speed estimation methods cannot estimate the speed of WLAN users accurately. For example, Doppler frequency cannot be measured directly since the WLAN access point local oscillator is not precise enough, and the transmit frequency is not known accurately. Also, the multi-path effect causes different path length for the received signals.
There are millions of commercial and private WLANs deployed so far and this number is growing everyday. Thus, it is desirable to use WLAN access points to estimate speed and direction of travel. The speed of a user can be estimated by using transitional features of radio waves received from one or more WLAN access points. Moving out of coverage area of some access points and entering coverage area of other access points maintains continuity of estimation over time.
Detecting zero speed is another challenging issue for WLAN users, since the environment surrounding a WLAN receiver in a metropolitan area is changing all the time, and as a result, the received signal characteristics is changing constantly.
A WLAN positioning system provides a systematic method and a methodology for gathering reference location data to enable a commercial positioning system using public and private 802.11 access points. Preferably, the data is gathered in a programmatic way to fully explore and cover the streets of a target region. The programmatic approach identifies as many Wi-Fi access points as possible.
FIG. 1 depicts a Wi-Fi positioning system (WPS). The positioning system includes positioning software [103] that resides on a computing device [101]. Throughout a particular coverage area there are fixed wireless access points [102] that broadcast information using control/common channel broadcast signals. The client device monitors the broadcast signal or requests its transmission via a probe request. Each access point contains a unique hardware identifier known as a MAC address. The client positioning software receives signal beacons from the 802.11 access points in range and calculates the geographic location of the computing device using characteristics from the signal beacons. Those characteristics include the unique identifier of the 802.11 access point, known as the MAC address, and the strengths of the signal reaching the client device. The client software compares the observed 802.11 access points with those in its reference database [104] of access points, which may or may not reside on the device as well. The reference database contains the calculated geographic locations and power profile of all the access points the gathering system has collected. The power profile may be generated from a collection of readings that represent the power of the signal from various locations. Using these known locations, the client software calculates the relative position of the user device [101] and determines its geographic coordinates in the form of latitude and longitude readings. Those readings are then fed to location-based applications such as friend finders, local search web sites, fleet management systems and E911 services.